We all know how machine learning is. It’s not easy to conduct anything related to machine learning. The duration and expense demanded by a machine learning application development project are not easy. It varies with the problem and the complexity of the algorithm. In this article, we’ll take you through the different parameters to estimate both the time and cost of your ML project. Read further to know more.
Time Estimation of Machine Learning Application Development
The time taken in a machine learning application development can be estimated by different phases of the project. These phases along with the approximate time consumed are divided into the following stages:
1. STAGE 1 – Discovery (7-14 days)
The construction of a plan for an ML project begins with stating the problem. The aim is to identify the issues and understand the requirements. Also, to see if machine learning is sufficient to meet these specific business goals.
This is the meeting stage where your team of developers meets the clients in order to understand their business objectives. It’s important to understand the problem they are searching a solution for.
Here, the development team also needs to check the availability of the data they require for the respective project.
Deliverable: A problem statement that categorizes the project as trivial or complicated.
2. STAGE 2 – Exploration (6-8 weeks)
At this stage, developers create a Proof of Principle, a documentation that can later be put in place as API. After the development of the baseline model, a team of Machine Learning experts determines the efficiency of the production-ready solution. This stage clarifies the level of performance you can expect following the standards set in the previous stage.
Deliverable: A Proof of Principle
STAGE 3 – Development (4+ months)
This is the phase where the team iteratively develops a solution until it’s ready for production. The uncertainties at this stage are not much so you expect the estimation to be pretty accurate.
Deliverable: ML solution ready for production
STAGE 4 – Improvement (Continuous)
Upon deployment, the stakeholders are always in haste to conclude the project in order to cut costs. But this formula is effective for only 80% of projects but is not applicable to machine learning apps.
AI models need constant monitoring as the data changes throughout the project timeframe. One thing that machine learning projects need for preferable results is time. Even if an algorithm works for a certain dataset, there are chances that it won’t perform the same on a different dataset.
Estimation of ML Application Cost
Given below are the major components that help estimate the ML application cost. Let’s take a look.
1. Data cost
Data is the major contribution to the currency of an ML project. The majority of research and its solutions rely on several trained learning models. It is a general knowledge that the more the trained learning, the more data is needed. Hence, the higher machine learning application development cost.
2. Research cost
When we talk about research for the project, it consists of the study, algorithm search, and experimentation. The exploration stage is something that every project passes through before production. Thoroughly following this part of the process incurs costs.
3. Production cost
In a machine learning project, production cost includes infrastructure cost, integration as well as maintenance cost. While cloud computation requires the least expense. Its cost depends on the varying complexity of each algorithm.
One major fragment that is often overlooked in machine learning app development is the consistent support it requires during the project lifecycle. This also demands significant cost.
It is not easy to estimate the cost and the time required in the development of a machine learning project. The success of a machine learning app depends on the amount of data it is fed. The more data, the better the outcome of your ML app. Additionally, your approach and your machine learning app development partner play a major role in the success.